import streamlit as st
import pandas as pd
from datetime import datetime
import joblib
from sklearn.base import BaseEstimator,
from datetime import datetime

# Load the trained model
def load_model():
    return joblib.load("src/best_model.joblib")

model = load_model()

# -------------------------------------------------
# Streamlit App Configuration
# -------------------------------------------------
st.set_page_config(
    page_title="Wellness Tourism Package Purchase Predictor",
    layout="centered"
)

st.title("Wellness Tourism Package Purchase Predictor")
st.markdown("""
Predict whether a customer will purchase our newly launched **Wellness Tourism Package**  
based on demographic and interaction details. Fill in the fields below and click **Predict**.
""")

# -------------------------------------------------
# Customer Demographics Inputs
# -------------------------------------------------
age = st.number_input("Age", min_value=18, max_value=100, value=30)
gender = st.selectbox("Gender", ["Male", "Female"])
marital_status = st.selectbox("Marital Status", ["Single", "Married", "Divorced"])
occupation = st.selectbox(
    "Occupation",
    ["Salaried", "Freelancer", "Business", "Student", "Retired", "Other"]
)
designation = st.text_input("Designation", value="")

city_tier = st.selectbox("City Tier", [1, 2, 3])
monthly_income = st.number_input(
    "Monthly Income (Gross)", min_value=0, step=1000, value=50000
)
passport = st.selectbox("Valid Passport?", ["Yes", "No"])
own_car = st.selectbox("Owns a Car?", ["Yes", "No"])
number_of_persons = st.number_input(
    "Number of People Visiting", min_value=1, max_value=10, value=2
)
number_of_children = st.number_input(
    "Number of Children Under 5 Visiting", min_value=0, max_value=5, value=0
)

# -------------------------------------------------
# Customer Interaction Inputs
# -------------------------------------------------
type_of_contact = st.selectbox(
    "Type of Contact", ["Company Invited", "Self Inquiry"]
)
product_pitched = st.selectbox(
    "Product Pitched",
    ["Wellness Package", "Adventure Package", "Cultural Package", "Other"]
)
pitch_satisfaction = st.slider(
    "Pitch Satisfaction Score", min_value=0, max_value=10, value=7
)
number_of_followups = st.number_input(
    "Number of Follow-ups", min_value=0, max_value=20, value=1
)
duration_of_pitch = st.number_input(
    "Duration of Pitch (minutes)", min_value=1, max_value=120, value=10
)
number_of_trips = st.number_input(
    "Average Number of Trips per Year", min_value=0, max_value=50, value=2
)
preferred_property_star = st.number_input(
    "Preferred Hotel Star Rating", min_value=1, max_value=5, value=3
)

# -------------------------------------------------
# Assemble Input DataFrame
# -------------------------------------------------
input_df = pd.DataFrame([{
    "Age": age,
    "Gender": gender,
    "MaritalStatus": marital_status,
    "Occupation": occupation,
    "Designation": designation,
    "CityTier": city_tier,
    "MonthlyIncome": monthly_income,
    "Passport": 1 if passport == "Yes" else 0,
    "OwnCar": 1 if own_car == "Yes" else 0,
    "NumberOfPersonVisiting": number_of_persons,
    "NumberOfChildrenVisiting": number_of_children,
    "TypeofContact": type_of_contact,
    "ProductPitched": product_pitched,
    "PitchSatisfactionScore": pitch_satisfaction,
    "NumberOfFollowups": number_of_followups,
    "DurationOfPitch": duration_of_pitch,
    "NumberOfTrips": number_of_trips,
    "PreferredPropertyStar": preferred_property_star
}])

# -------------------------------------------------
# Prediction & Display
# -------------------------------------------------
if st.button("Predict Purchase"):
    pred = model.predict(input_df)[0]
    label = "🚀 Will Purchase" if pred == 1 else "❌ Will Not Purchase"
    st.subheader("Prediction Result")
    st.success(label)